scholarly journals Potential of Highly Automated Vehicles for Monitoring Fatigued Drivers and Explaining Traffic Accidents on Motorway Sections

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Hyun-ho Chang ◽  
Byoung-jo Yoon
Keyword(s):  
Autonomous Vehicles ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Explanatory Power ◽  
Real Life ◽  
Statistical Correlation ◽  
Trajectory Data ◽  
Road Section ◽  
Accident Data ◽  
Vehicle Trajectory

The near-future deployment of high-level automation vehicles (AVs) can render promising opportunities to solve ongoing hindrances in modern safety-related research. Monitoring fatigued drivers on any road section is one of these challenges. Vehicle trajectory big data, monitored through AVs, include key information with which to monitor fatigued drivers on roads. To mine this upcoming opportunity, a new data-driven approach which allows the direct monitoring of fatigued drivers on road segments is proposed here for the first time. A feasible study was conducted using big vehicle trajectory data and real-life traffic accident data. The results showed that fatigued drivers on a target road section can be successfully surveyed using the driving durations from departure locations to the target road section. It was found that, with a statistical correlation of 0.90, an index for fatigued drivers has strong explanatory power about the traffic accident rate. This finding indicates that the proposed method will be a promising means by which to monitor fatigued drivers at road locations in the upcoming era of autonomous vehicles. In addition, the method is immediately practicable if vehicle trajectory data are available.

scholarly journals Potentialities of Vehicle Trajectory Big Data for Monitoring Potentially Fatigued Drivers and Explaining Vehicle Crashes on Motorway Sections

2020 ◽  
Vol 12 (15) ◽  
pp. 5877
Author(s):  
Hyunho Chang ◽  
Dongjoo Park
Keyword(s):  
Big Data ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Explanatory Power ◽  
Real Life ◽  
Statistical Correlation ◽  
Vehicle Crashes ◽  
Trajectory Data ◽  
Accident Data ◽  
Vehicle Trajectory

Task-related fatigue, caused by prolonged driving, is a major cause of vehicle crashes. Despite noticeable academic achievements, monitoring drivers’ fatigue on road sections is still an ongoing challenge which must be met to prevent and reduce traffic accidents. Fortunately, individual instances of vehicle trajectory big data collected through advanced vehicle-GPS systems offer a strong opportunity to trace driving durations. We propose a new approach by which to monitor task-related fatigued drivers by directly using the ratio of potentially fatigued drivers (RFD) to all drivers for each road section. The method used to compute the RFD index was developed based on two inputs: the distribution of the driving duration (extracted from vehicle trajectory data), and the boundary condition of the driving duration between fatigued and non-fatigued states. We demonstrate the potentialities of the method using vehicle trajectory big data and real-life traffic accident data. Results showed that the measured RFD has a strong explanatory power with regard to the traffic accident rate, with a statistical correlation of 0.86 at least, for regional motorway sections. Therefore, it is expected that the proposed approach is a feasible means of successfully monitoring fatigued drivers in the present and near future era of smart-mobility big data.

scholarly journals Comparative Study of Machine Learning Classifiers for Modelling Road Traffic Accidents

2022 ◽  
Vol 12 (2) ◽  
pp. 828
Author(s):  
Tebogo Bokaba ◽  
Wesley Doorsamy ◽  
Babu Sena Paul
Keyword(s):  
Machine Learning ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Real Life ◽  
Support Vector ◽  
Road Traffic Accidents ◽  
Machine Learning Classifiers ◽  
Reduction Techniques ◽  
Learning Classifiers ◽  
Accident Data

Road traffic accidents (RTAs) are a major cause of injuries and fatalities worldwide. In recent years, there has been a growing global interest in analysing RTAs, specifically concerned with analysing and modelling accident data to better understand and assess the causes and effects of accidents. This study analysed the performance of widely used machine learning classifiers using a real-life RTA dataset from Gauteng, South Africa. The study aimed to assess prediction model designs for RTAs to assist transport authorities and policymakers. It considered classifiers such as naïve Bayes, logistic regression, k-nearest neighbour, AdaBoost, support vector machine, random forest, and five missing data methods. These classifiers were evaluated using five evaluation metrics: accuracy, root-mean-square error, precision, recall, and receiver operating characteristic curves. Furthermore, the assessment involved parameter adjustment and incorporated dimensionality reduction techniques. The empirical results and analyses show that the RF classifier, combined with multiple imputations by chained equations, yielded the best performance when compared with the other combinations.

scholarly journals ROAD SAFETY CAMPAIGNS TO REDUCE TRAFFIC ACCIDENTS FOR YOUNG ROAD USERS

ICCD ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 601-606
Author(s):  
Widodo Budi Dermawan ◽  
Dewi Nusraningrum
Keyword(s):  
School Environment ◽  
Road Safety ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Road Traffic ◽  
School Administrators ◽  
Road User ◽  
The Road ◽  
Road Users ◽  
Accident Data

Every year we lose many young road users in road traffic accidents. Based on traffic accident data issued by the Indonesian National Police in 2017, the number of casualties was highest in the age group 15-19, with 3,496 minor injuries, 400 seriously injured and 535 deaths. This condition is very alarming considering that student as the nation's next generation lose their future due to the accidents. This figure does not include other traffic violations, not having a driver license, not wearing a helmet, driving opposite the direction, those given ticket and verbal reprimand. To reduce traffic accident for young road user, road safety campaigns were organized in many schools in Jakarta. This activity aims to socialize the road safety program to increase road safety awareness among young road users/students including the dissemination of Law No. 22 of 2009 concerning Road Traffic and Transportation. Another purpose of this program is to accompany school administrators to set up a School Safe Zone (ZoSS), a location on particular roads in the school environment that are time-based speed zone to set the speed of the vehicle. The purpose of this paper is to promote the road safety campaigns strategies by considering various campaign tools.

scholarly journals MOBILE-WEB-BASE VOLUNTEERED GEOGRAPHIC INFORMATION APPLICATION AND GEOMETRIC ACCURACY ANALYSIS FOR TRAFFIC ACCIDENTS

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 375-378
Author(s):  
H. K. Sevinc ◽  
I. R. Karas ◽  
E. Demiral
Keyword(s):  
Traffic Accident ◽  
Traffic Accidents ◽  
Geographic Information ◽  
Google Earth ◽  
Geographical Information ◽  
Accuracy Analysis ◽  
Geometric Accuracy ◽  
Web Based ◽  
Mobile Web ◽  
Accident Data

Abstract. The users can contribute to geographic information through platforms such as Wikimapia and OpenStreetMap. They can also generate data by themselves with their applications in cyber worlds like Google Earth. This study is primarily designed to be a guide regarding Volunteered Geographical Information (VGI) and to evaluate the geometric accuracy of data collected from volunteers on application. The main purpose of this study is to present basic information about Volunteered Geographical Information (VGI), why users are tending to use VGI, the accuracy of the data entered by the user, to examine the examples of use in various fields, to learn about geographic information systems and to compare this phenomenon and also by developing a VGI application to examine the similarity between the actual data and the data collected from volunteer users. A mobile and web-based application have been developed to collect traffic accident data from volunteer users. The geometric accuracy analysis was performed by comparing the data collected with this application with the data obtained from the General Directorate of Security.

scholarly journals K-Means Clustering untuk Data Kecelakaan Lalu Lintas Jalan Raya di Kecamatan Pelaihari

2018 ◽  
Vol 5 (5) ◽  
pp. 613 ◽  
Author(s):  
Winda Aprianti ◽  
Jaka Permadi
Keyword(s):  
Data Mining ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Distance Measure ◽  
Data Traffic ◽  
First Aid Training ◽  
Accident Data ◽  
Special Concern ◽  
Clustering Data ◽  
Minor Injuries

<p>Kecelakaan lalu lintas di jalan raya masih menjadi penyumbang tingginya angka kematian di Indonesia, sehingga menjadi perhatian khusus bagi kepolisian di negara ini. Termasuk Kepolisian Resor (Polres) Tanah Laut, yang telah membuktikan perhatian tersebut dengan membentuk komunitas korban kecelakaan lalu lintas dan Pelatihan Pertolongan Pertama Gawat Darurat (PPGD). Tahapan awal pencegahan kecelakaan lalu lintas adalah dengan mengetahui faktor-faktor penyebab kecelakaan lalu lintas yang diperoleh melalui analisa data kecelakaan. Analisa tersebut dapat dilakukan dengan data mining, yaitu <em>K-Means Clustering.</em> <em>K-Means Clustering</em> mengelompokkan data menjadi beberapa <em>cluster</em> sesuai karakteristik data tersebut. Data kecelakaan lalu lintas dibagi menjadi 2 dataset, yakni dataset 1 dan dataset 2. Hasil <em>cluster </em>penerapan <em>K-means clustering </em>terhadap dataset 1 dan dataset 2 kemudian dilakukan pengujian <em>silhoutte coefficient </em>untuk mencari hasil <em>cluster </em>dengan kualitas terbaik<em>. </em>Pengujian <em>silhoutte coefficient</em> secara berurutan menghasilkan <em>distance measure </em>paling optimal yakni <em>clustering </em>dengan 4 <em>cluster</em> untuk dataset 1 dan <em>clustering </em>dengan 2 <em>cluster</em> untuk dataset 2. Selain memperoleh <em>cluster </em>dengan kualitas terbaik, penganalisaan data juga menghasilkan beberapa informasi kecelakaan lalu lintas yang sering terjadi, yakni faktor penyebab dan korban kecelakaan adalah pengemudi, umur korban adalah 9 sampai 28 tahun, dan keadaan korban kecelakaan adalah luka ringan.</p><p> </p><p class="Judul2"><strong><em>Abstract</em></strong></p><p><em>Traffic accidents on the highway are still contribute to the high mortality rate in Indonesia, which are becoming a special concern for the police. Including the Police of Tanah Laut Resort where prove themselves by established The Community of Traffic Accident Victims and Emergency First Aid Training. The first prevention of traffic accidents is knowing the factors causing traffic accidents which is obtained through the analysis of traffic accident’s data. It can be done through data mining, i.e. K-Means Clustering, which is clustering data into clusters according to characteristics of the data. Traffic accident data is divided into two datasets, namely dataset 1 and dataset 2. After obtaining the cluster results, the next step is to calculate silhoutte coefficient which is used to find the best quality cluster result. The result of testing silhoutte coefficient are clustering with 4 clusters for dataset 1 and clustering with 2 clusters for dataset 2. Analyzing data in this research also produces some information on traffic accidents that often occur, namely the causes and victims of accidents are drivers, the age of the victims is between 9 and 28 years old, and the circumstance of the accidents victims are minor injuries.</em></p>

scholarly journals Contribution to Accident Prediction Models Development for Rural Two-Lane Roads in Serbia

2016 ◽  
Vol 28 (4) ◽  
pp. 415-424 ◽  
Author(s):  
Draženko Glavić ◽  
Miloš Mladenović ◽  
Aleksandar Stevanovic ◽  
Vladan Tubić ◽  
Marina Milenković ◽  
...  
Keyword(s):  
Multivariate Regression ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Prediction Models ◽  
Future Research ◽  
Practical Application ◽  
Accident Prediction ◽  
Accident Data ◽  
Road Characteristics ◽  
The Relationship

Over the last three decades numerous research efforts have been conducted worldwide to determine the relationship between traffic accidents and traffic and road characteristics. So far, the mentioned studies have not been carried out in Serbia and in the region. This paper represents one of the first attempts to develop accident prediction models in Serbia. The paper provides a comprehensive literature review, describes procedures for collection and analysis of the traffic accident data, as well as the methodology used to develop the accident prediction models. The paper presents models obtained by both univariate and multivariate regression analyses. The obtained results are compared to the results of other studies and comparisons are discussed. Finally, the paper presents conclusions and important points for future research. The results of this research can find theoretical as well as practical application.

scholarly journals Pedestrian Throw Distance Prediction from Vehicle Damage Intensity

2020 ◽  
Vol 32 (3) ◽  
pp. 371-382
Author(s):  
Nenad Saulić ◽  
Zoran Papić ◽  
Zoran Ovcin
Keyword(s):  
Traffic Accident ◽  
Traffic Accidents ◽  
Real Life ◽  
Empirical Models ◽  
Ordinal Scale ◽  
Vehicle Speed ◽  
Vehicle Impact ◽  
Throw Distance ◽  
Distance Prediction ◽  
Pedestrian Collisions

One of the main points to be addressed when analysing vehicle-pedestrian collisions is the vehicle impact speed. If the traffic accident is not recorded on camera, and there are no skid marks nor tachograph in the vehicle, the parameter is determined on the basis of empirical models. All empirical models for ascertaining vehicle speed are based on the pedestrian throw distance, which is not always known because of an unidentified vehicle-pedestrian collision point or the final rest position of the pedestrian after collision. This paper shows a description of a vehicle damage recorded in an ordinal scale and determines the pedestrian throw distance prediction model from the vehicle damage established in such a way. If the accident scene is documented by photographs, the damage can be classified, and by applying a validated model, the pedestrian throw distance envisaged. Then, by applying an empirical model, one can determine the speed of the vehicle at the time of collision with a pedestrian. Two databases were formed during the research. The first is based on real-life traffic accidents (expert witnessing of the professors from the Faculty of Technical Sciences). The second is based on traffic accident simulations as part of PC Crash software package.

scholarly journals Traffic accident analysis based on C4.5 algorithm in WEKA

2019 ◽  
Vol 272 ◽  
pp. 01035 ◽  
Author(s):  
Jiajia Li ◽  
Jie He ◽  
Ziyang Liu ◽  
Hao Zhang ◽  
Chen Zhang
Keyword(s):  
Traffic Safety ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Safety Management ◽  
Depth Information ◽  
Highway Traffic ◽  
Safety Issues ◽  
C4.5 Algorithm ◽  
Accident Data ◽  
The Impact

At present, China is in a period of steady development of highways. At the same time, traffic safety issues are becoming increasingly serious. Data mining technology is an effective method for analysing traffic accidents. In-depth information mining of traffic accident data is conducive to accident prevention and traffic safety management. Based on the data of Wenli highway traffic accidents from 2006 to 2013, this study selected factors including time factor, linear factor and driver characteristics as research indicators, and established the decision tree using C4.5 algorithm in WEKA to explore the impact of various factors on the accident. According to the degree of contribution of each variable to the classification effect of the model, various modes affecting the type of the accident are obtained and the overall prediction accuracy is about 80%.

Analysis Traffic Accident Prone Areas Using Cluster Analysis in Bogor City Based on WebGIS

2021 ◽  
pp. 55-62
Author(s):  
Lulu Lutfi Latifah
Keyword(s):  
Cluster Analysis ◽  
Information System ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Cluster Algorithm ◽  
Geographical Information ◽  
Prone Area ◽  
Accident Data ◽  
The City ◽  
City Police

Traffic accidents are a problem that occurs in various regions in Indonesia, especially in the city of Bogor. Based on traffic accident data obtained from the Laka Unit, the Bogor City Police experienced fluctuating movements. The use of accident data is also not optimal. This makes it difficult to see areas that have a level of vulnerability. To solve this problem, in this study an analysis was made to determine the areas prone to traffic accidents by utilizing the Geographical Information System to map the distribution of locations. The method used to analyze the accident area is using the K-Means Cluster Algorithm. The results of the research conducted showed that the highest level of vulnerability from 2014 to 2019 was in the sub-district of Tanah Sareal on Jalan K.H. Sholeh Iskandar. Several incidents of laka occurred on curves, bypasses, and in and out of vehicles. The result of this research is in a beautiful traffic accident prone area in the form of WebGIS.

scholarly journals Exploring the Application of the Linear Poisson Autoregressive Model for Analyzing the Dynamic Impact of Traffic Laws on Fatal Traffic Accident Frequency

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Yue Zhang ◽  
Yajie Zou ◽  
Lingtao Wu ◽  
Jinjun Tang ◽  
Malik Muneeb Abid
Keyword(s):  
Traffic Safety ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Negative Binomial ◽  
Moving Average ◽  
Red Light ◽  
Dynamic Impact ◽  
Temperature And Precipitation ◽  
Accident Data ◽  
Poisson Autoregressive Model

Annual fatal traffic accident data often demonstrate time series characteristics. The existing traffic safety analysis approaches (e.g., negative binomial (NB) model) often cannot accommodate the dynamic impact of factors in fatal traffic accident data and may result in biased parameter estimation results. Thus, a linear Poisson autoregressive (PAR) model is proposed in this study. The objective of this study is to apply the PAR model to analyze the dynamic impact of traffic laws and climate on the frequency of fatal traffic accidents occurred in a large time span (from 1975 to 2016) in Illinois. Besides, the NB model, NB with a time trend, and autoregressive integrated moving average model with exogenous input variables (ARIMAX) are also developed to compare their performances. The important conclusions from the modelling results can be summarized as follows. (1) The PAR model is more appropriate for analyzing the dynamic impacts of traffic laws on annual fatal traffic accidents, especially the instantaneous impacts. (2) The law that allows motorcycles and bicycles to proceed on a red light following the rules applicable after a “reasonable period of time” leads to an increase in the frequency of annual fatal traffic accidents by 14.98% in the short term and 30.69% in the long term. The climate factors such as average temperature and precipitation concentration period have insignificant impacts on annual fatal traffic accidents in Illinois. Thus, the modelling results suggest that the PAR model is more appropriate for annual fatal traffic accident data and has an advantage in estimating the dynamic impact of traffic laws.

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